Machine learning based optimization for vehicle-to-infrastructure communications
Xiang, Wei, Huang, Tao, and Wan, Wanggen (2019) Machine learning based optimization for vehicle-to-infrastructure communications. Future Generation Computer Systems, 94. pp. 488-495.
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Abstract
In this paper, we study wireless communications in vehicle-to-infrastructure communications. In certain situations, multiple vehicles within a local range need to exchange information via common roadside infrastructure. Example scenarios include busy intersections, and a driver with the knowledge of information from other vehicles can make safer decisions. Fast and reliable communications are essential in such use cases. We consider two different system models in this paper. In the first model, we consider the case where both the base station and vehicles are equipped with a single antenna. In the second model, we discuss the case where multiple antennas are installed on both the base station and vehicles. We show how the system can be optimized in both cases. We then discuss how machine learning can be adopted in both models to realize the optimized system performance. (C) 2018 Elsevier B.V. All rights reserved.
Item ID: | 57816 |
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Item Type: | Article (Research - C1) |
ISSN: | 0167-739X |
Keywords: | Vehicle-to-infrastructure, Internet of vehicles, Single-antenna system, Multi-antenna system, Machine learning, Information exchange |
Copyright Information: | © 2018 Elsevier B.V. All rights reserved. |
Funders: | National Natural Science Foundation of China |
Projects and Grants: | NNSFC grant 61628102 |
Date Deposited: | 03 Apr 2019 07:50 |
FoR Codes: | 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation @ 50% 10 TECHNOLOGY > 1005 Communications Technologies > 100510 Wireless Communications @ 50% |
SEO Codes: | 89 INFORMATION AND COMMUNICATION SERVICES > 8901 Communication Networks and Services > 890199 Communication Networks and Services not elsewhere classified @ 100% |
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